simulation models Nick Malleson and Andy Evans Project Background Started as a PhDMSc Project Build and agentbased model which we can use to predict rates of residential burglary Individuallevel person household ID: 179984
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Slide1
Understanding and preventing crime: A new generation ofsimulation models
Nick
Malleson
and Andy EvansSlide2
Project Background
Started as a PhD/MSc Project
“Build and agent-based model which we can use to predict rates of residential burglary”
Individual-level (person, household).
Predict effects of physical/social changes on burglary.
Ongoing relationship with Safer Leeds CDRP
Provide essential data.
Expert knowledge supplement criminology theory.Slide3
Theoretical Background
Crimes are local in nature.
Routine Activities Theory
convergence in space and time of a motivated offender and a victim in the absence of a capable guardian.
Crime Pattern Theory
people will commit crimes in areas they know well and feel safe in;
everyone has a cognitive map of their environment;
anchor points shape these “activity spaces”.
Need to work at the level of the individualSlide4
Agent-Based Modelling (ABM)
Autonomous, interacting agents
Represent individuals or groups
Situated in a virtual environmentSlide5
Advantages of ABM (i)
More “natural” for social systems than statistical approaches.
Can include physical space / social processes in models of social systems.
Designed at abstract level: easy to change scale.
Bridge between verbal theories and mathematical models.Slide6
Advantages of ABM (ii)
Dynamic history of systemSlide7
Disadvantages of ABM
Single model run reveals a theorem, but no information about robustness.
Sensitivity analysis and many runs required.
Computationally expensive.
Small errors can be replicated in many agents.
“Methodological individualism”.
Modelling “soft” human factors.Slide8
An Example Agent-Based Model of Burglary
Virtual Environment
Physical
objects: houses,
roads
, bars, busses
etc.
Social
attributes: “communities”Virtual victims and guardiansVirtual Burglar AgentsUse
criminology theories/findings to build realistic agent behaviourSlide9
The Environment –
layersSlide10
The Burglars
Needs
“
Lifestyle”, Sleep,
Drugs
Cognitive map of environment
Decision process leads to burglarySlide11Slide12
Interesting Finding – Halton Moor
Result
Halton
Moor area significantly under predicted by model
Explanation
Motivations of burglars in
Halton
Moor
Model failures can help to indicate where we misunderstand the real worldSlide13
Results:Simulating Urban Regeneration
Simulation
Test the effects of a large urban regeneration scheme
A small number of individual houses were identified as having substantially raised risk
Why?
Location on main road
In the awareness space of offenders
Slightly more physically vulnerable
Need for a realistic, individual-level model to predict crimeSlide14
Who else is doing this?
Researchers:
Elizabeth Groff: street robbery
Daniel Birks: burglary
Patricia Brantingham
et al
.: Mastermind (exploring theory)
Lin Liu, John Eck, J Liang,
Xuguang Wang: cellular automata
Books / Journals:
Artificial Crime Analysis Systems
(Liu and Eck, 2008)Special issue of the Journal of Experimental Criminology
(2008)
:
``Simulated Experiments in Criminology and Criminal Justice'Slide15
GeoCrimeDatahttp://
geocrimedata.blogspot.co.uk
/
Project Overview
Improve access and usability of spatial data to crime analysts
Motivation: Are cul-de-sacs safer? (Johnson & Bowers,2010)
Collaboration
between Leeds & Huddersfield (Alex Hirschfield, Andrew Newton
)
MethodologySurvey practitionersIdentify useful dataAnalyse and re-release data publiclyResultsNew road accessibility dataHousehold vulnerability dataSlide16
Road accessibility estimates
Building typesSlide17
More information
General info:
http://crimesim.blogspot.com
/
Play with a simple tutorial version of the model:
http://
code.google.com/p/repastcity/
Papers:
http://
www.geog.leeds.ac.uk/people/n.mallesonhttp://
www.geog.leeds.ac.uk/people/a.evansGeoCrimeData project:
http://geocrimedata.blogspot.com/